Image processing

ABSTRACT

One aspect of the invention concerns a method and system for processing an image recorded by an imaging device. The method comprises the steps of:- a) providing an image comprising an array of adjacent elements each corresponding to a respective part of the image and having a respective intensity value associated therewith ( 400 ); b) processing the intensity values to determine an intensity contrast value for each respective element according to differences in intensity values between respective adjacent elements ( 402 ); c) determining a depth value for each element corresponding to the distance between the imaging device and at least part of an image forming object represented in the image by the respective element ( 404 ); d) processing the depth values to determine a depth contrast value for each respective element according to differences in depth values between respective adjacent elements ( 406 ); and, processing said intensity contrast values and said depth contrast values to identify at least one area of the image corresponding to one or more respective objects in the image being processed ( 408 ). Step ( 408 ) can be a non-linear diffusion process whereby variations in grey level values are diffused in regions corresponding to objects or background and enhanced in regions corresponding to object boundaries.

[0001] This invention relates to image processing and in particular to asystem and method for processing digital images to detect and extractphysical entities or objects represented in the image.

[0002] Image segmentation is of fundamental importance to many digitalimage-processing applications. The process of image segmentation refersto the grouping together of parts of an image that have similar imagecharacteristics and this is often the first process in image processingtasks. For instance, in the field of video coding it is often desirableto decompose an image into an assembly of its constituent objectcomponents prior to coding. This pre-processing step of imagesegmentation then allows individual objects to be coded separately.Hence, significant data compression can be achieved in video sequencessince slow moving background can be transmitted less frequently thanfaster moving objects.

[0003] Image segmentation is also important in the field of imageenhancement, particularly in medical imaging such as radiography. Imagesegmentation can be used to enhance detail contained in an image inorder to improve the usefulness of the image. For instance, filteringmethods based on segmentation have been developed for removing noise andrandom variations in intensity and contrast from captured digital imagesto enhance image detail and assist human visualisation and perception ofthe image contents.

[0004] Other fields where image segmentation is important includemulti-media applications such as video indexing and post productioncontent-based image retrieval and interpretation, that is to say videosequence retrieval based on user supplied content parameters and machinerecognition and interpretation of image contents based on suchparameters.

[0005] Fundamental to image segmentation is the detection of homogeneousregions and/or the boundaries of such regions which represent objects inthat image. Homogeneity may be detected in terms of intensity ortexture, that is grey level values, motion (for video sequences),disparity (for stereoscopic images), colour, and/or focus for example.Many approaches to image segmentation have been attempted includingtexture-based, intensity-based, motion-based and focus-basedsegmentation. Known approaches require significant computationalresources and often provide unsatisfactory results.

[0006] One approach that uses intensity or grey level values for objectsegmentation is thresholding. The concept of image segmentation based onthresholding is described in the paper “An Amplitude Segmentation MethodBased on the Distribution Function of an Image”, Compute, Vision,Graphics and Image Processing, 29, 47-59, 1985. In the thresholdingmethod intensity values are determined for each pixel or picture elementin a digital image and on the basis of these values a threshold value isdetermined that distinguishes each pixel of an object in the image frompixels representing background detail. In practice, the thresholdintensity value is determined dynamically for each image according tothe statistical distribution of intensity values, that is to say, thevalue is based on a histogram analysis of all the intensity values for aparticular image. Peaks in the histogram distribution generallyrepresent intensity values predominately associated with a particularobject. If two objects are present in an image there will be two peaks.In these circumstances the intersection or overlap between the two peaksis taken as the threshold value. This approach to image segmentation isrelatively straightforward but can be computationally intensiveparticularly when complex images are presented, for example, imagescomprising a number of objects or complex backgrounds or when the imageis heavily “textured”, that is to say, the image comprises a numberseparate regions within an object that have different intensity values.When textured images are processed using threshold-based methods“over-segmentation” can occur, that is, regions within an object arethemselves recognised as separate objects within the image beingprocessed.

[0007] The problem of over segmentation can be partially overcome if theimage is simplified prior to thresholding. Image simplification involvesthe removal of low order intensity value differences between adjacentpixels within an object boundary while the intensity value differencesare maintained at the object boundaries. Image simplification is oftenachieved in digital image processing by using so called non-lineardiffusion methods. The concept of non-linear diffusion for imageprocessing is described in the published paper “Scale Space and EdgeDetection Using Anisotropic Diffusion”, IEEE Trans. on Pattern Analysisand Machine Intelligence Vol.12 No. 7 pp629-639, July 1990. In thismethod pixel intensities are altered in a manner analogous to diffusionof physical matter to provide regions of homogenous intensity withinobject boundaries while preventing diffusion at the object boundaries,thereby preserving intensity contrast at the boundaries. It has beenfound, however, that methods of image simplification based on knownnon-linear diffusion algorithms result in over segmentation.

[0008] According to a first aspect of the invention there is provided amethod of processing an image recorded by an imaging device; said methodcomprising the steps of:

[0009] a) providing an image comprising an array of adjacent elementseach corresponding to a respective part of the image and having arespective intensity value associated therewith;

[0010] b) processing said intensity values to determine an intensitycontrast value for each respective element according to differences inintensity values between respective adjacent elements;

[0011] c) providing a depth value for each element corresponding to thedistance between the imaging device and at least part of an imageforming object represented in the image by the respective element;

[0012] d) processing said depth values to determine a depth contrastvalue for each respective element according to differences in depthvalues between respective adjacent elements; and,

[0013] e) processing said intensity contrast values and said depthcontrast values to identify at least one area of the image correspondingto one or more respective objects in the image being processed.

[0014] Thus, by processing depth contrast values with the intensitycontrast values, data relating to the relief of an image, that is thedepth of image forming objects (or more precisely the distance travelledby the reflected incident radiation) in the image, can be used toimprove object boundary detection and thereby improve segmentation ofthe image into its constituent object parts. By using two parametersinstead of one the accuracy of determining object boundaries can besignificantly improved.

[0015] Preferably, said depth values are determined according to thespacing between corresponding points on a stereoscopic image pair. Thespacing between corresponding points can be readily converted into depthvalues based on known imaging system geometry. Hence, additional imageprocessing can be minimised.

[0016] In a preferred embodiment, the spacing between said points isdetermined by matching said corresponding points and estimating a vectorvalue for the relative spacing and direction of said points. In thisway, respective vector values can be used to represent the respectivedepth values associated with the respective elements.

[0017] Conveniently, said area is determined by identifying an outlineof said respective object or objects in the image. This readily providesfor object identification.

[0018] Preferably, step e) comprises the step of altering the intensityvalues of each respective element in accordance with the respectiveintensity contrast value and the respective depth contrast value of theelement. This can increase intensity differences between adjacentelements at the respective object boundaries.

[0019] In preferred embodiments, the step of altering the intensityvalues comprises the step of modifying the respective intensity contrastvalues according to the respective depth contrast values, and alteringthe intensity values of the respective elements towards an averageintensity value determined by the intensity values of surroundingelements if the respective modified intensity contrast value of theelement is below a threshold value. This improves image simplificationby reducing differences in intensity values between elementscorresponding to positions within an object.

[0020] Conveniently, the intensity contrast values are modified suchthat elements having a higher than average depth contrast value havetheir respective intensity values altered less than elements having alower than average depth contrast value. This increases the differencein the intensity values between adjacent elements corresponding topositions on opposing sides of object boarders.

[0021] Preferably, step e) comprises a non-linear diffusion process foraltering element intensity values in accordance with respectiveintensity contrast values modified in accordance with respective depthcontrast values. In this way, it is possible to improve known non-lineardiffusion methods of image simplification by modifying the diffusionprocess in accordance with further object identifying data, that is tosay using the depth data associated with each element.

[0022] In a preferred embodiment, the method further comprises the stepof delineating said object or objects from the image. This allows theobjects to be stored, retrieved, coded, or processed separately, forexample.

[0023] Preferably, the delineating step comprises the steps of:

[0024] determining a statistical distribution function of the alteredintensity values of the respective elements;

[0025] determining a threshold value or range of values to include allthe intensity values of the respective elements of at least oneidentified area in the image; and,

[0026] selecting elements having modified intensity values within thethreshold range or above or below said threshold value; and

[0027] delineating image data relating to said selected elements fromimage data relating to the remaining elements. In this way it ispossible to implement relatively simple thresholding methods to extractthe object or objects from the processed image.

[0028] According to a second aspect of the invention there is provided amethod of processing an image in accordance with a non-linear diffusionprocess; said method comprising the steps of:

[0029] i) providing an image comprising an array of adjacent elementseach corresponding to a respective part of the image and having arespective intensity value associated therewith;

[0030] ii) processing said intensity values to determine an intensitycontrast value for each respective element according to differences inintensity values between respective adjacent elements;

[0031] iii) identifying from said array of elements object definingelements corresponding to points on respective objects in the image;

[0032] iv) altering said element intensity values in accordance saidrespective intensity contrast values, whereby said elements are alteredto a lesser or greater extent in dependence on whether said respectiveelement is an object element.

[0033] In one embodiment, step iii) comprises the step of determining adepth value associated with a disparity field for each element in theimage and identifying said object elements from said depth values.

[0034] In another embodiment, step iii) comprises the step ofdetermining a motion value associated with a motion vector in a videosequence for each element in the image and identifying said objectelements from said motion values. Accordingly, the non-linear diffusionprocess can be modified in accordance with object positions determinedby motion recorded in a video sequence.

[0035] According to a third aspect of the present invention there isprovided an image processing system for processing an image recorded byan imaging device; said system comprising:

[0036] a) a data receiver for receiving data relating to an imagecomprising an array of adjacent elements each corresponding to arespective part of the image and having a respective intensity valueassociated therewith;

[0037] an intensity value processor configured to determine an intensitycontrast value for each respective element according to differences inintensity values between respective adjacent elements;

[0038] a depth value processor configured to determine a depth value foreach element corresponding to the distance between the imaging deviceand at least part of an image forming object represented in the image bythe respective element;

[0039] a depth contrast value processor configured to determine a depthcontrast value for each respective element according to differences indepth values between respective adjacent elements; and,

[0040] an object segment processor configured to process intensitycontrast values and said depth contrast values to identify at least onearea of the image corresponding to one or more respective objects in theimage being processed.

[0041] The invention will now be described, by way of example only, withreference to the accompanying drawings; in which:

[0042]FIG. 1 is a schematic block diagram of a system for processingdigital images;

[0043]FIG. 2a shows a pair of stereoscopic images of a scene viewed fromtwo different perspectives with a stereoscopic imaging device;

[0044]FIG. 2b shows the images of FIG. 2 in side by side relation;

[0045]FIG. 3 is a schematic block diagram of an image processor forprocessing digital images in the system of FIG. 1;

[0046]FIG. 4 is a flow chart of a method for processing digital images

[0047]FIG. 5a is a pre-processed image of a scene comprising an objectto be segmented;

[0048]FIG. 5b is a processed image of the image of FIG. 5a processed inaccordance with a known non-linear diffusion process;

[0049]FIG. 5c is a processed image of the image of FIG. 5a showingdisparity or depth vectors for the image of FIG. 5a obtained from astereoscopic image pair;

[0050]FIG. 5d is a processed image of the image of FIG. 5a processed inaccordance with a modified non-linear diffusion process utilising thedisparity data represented in FIG. 5c; and,

[0051]FIG. 5e is shows an object mask extracted from the processed imageof FIG. 5a.

[0052] With reference to FIG. 1, in one arrangement of the presentinvention an image processor 102 is arranged to receive digital imagesfrom a memory 104 storing two dimensional images of three dimensionalscenes recorded by means of an optical-electronic imaging device 106.The imaging device 106 receives electromagnetic radiation from all areasof the scene being recorded including one or more distinct image formingobjects 108 within the imaging device's field of view 110. The imagingdevice can be any device capable of forming optical-electronic images,including for example an array of light sensitive photo-diodes or thelike connected to respective charged coupled devices for forming adigital image of picture elements or pixels capable of being stored inelectronic digital memory 104. The pixels each have a grey level valueassociated with them representative of the brightness or intensity ofthe respective part of the scene they represent. Data relating to thecolour associated with each pixel may also be stored in the memory 104.

[0053] In the present arrangement the imaging device comprises twoseparate optical-electronic imaging systems for recording stereoscopicimage pairs. FIG. 2a shows a pair of images, 200 to the left of thedrawing and 202 to the right, that define a stereoscopic image paircorresponding to two different perspective projections in slightlydifferent planes of the same scene. The image processor 102 isprogrammed in a known manner to process stereoscopic image pairs of thetype shown to obtain data relating to the depth of the or each objectand the background in a scene, or more precisely, the distance travelledby the incident electromagnetic radiation reflected by the or eachobject or background to the respective light sensitive pixels of theimaging device. The image processor is programmed to determine disparityvectors in much the same way that conventional image processes areprogrammed to determine motion vectors for object segmentation prior tovideo sequence coding. For instance, depth is estimated from thestereoscopic images by estimating a disparity vector for each pair ofcorresponding points in the image pair. In FIG. 2 a point 204 on anobject in a scene has a position defined by the spatial co-ordinates(x,y,z). This point is projected on the left image at a point 206 havingthe local spatial co-ordinates (x,y)l and likewise on the right image ata point 208 having the spatial co-ordinates (x,y)r. The left and rightimages have the same co-ordinate reference frame and so the distance anddirection between the two corresponding points 206 and 208, known as thedisparity vector, can be readily determined.

[0054]FIG. 2b shows the two images 200 and 202 in side by side relation.The disparity vector 210 for corresponding points 206 and 208 is shownon the right hand image 202. The vector extends between the projectedpoint 206 of image 200 and point 208 on image 202.

[0055] It is possible to determine the distance of a point in an imagefrom the disparity vector for that point based on knowledge of theimaging system geometry. The estimation of depth in an image usingstereoscopic imaging is described in detail in the paper “Depth BasedSegmentation” IEEE Transaction on Circuits and Systems for VideoTechnology, 7(1), February 1997, pp237-239.

[0056] In the arrangement of FIG. 3, the image processor 102 comprises adata receiving interface 302 for receiving data defining stereoscopicimage pairs of a scene or sequence of scenes from the memory 104. Thedata-receiving interface is connected to a first processor 304 which isprogrammed to determine an intensity contrast value for each of thepixels in one or both stereoscopic images. The intensity contrast valueis the intensity or grey level gradient at the respective pixeldetermined by the local variation in intensity in the adjacent pixels.The receiving interface is also connected to a second processor 306which includes a first module 308 programmed to determine the disparityvector associated with each pixel and a second module 310 programmed todetermine a disparity or depth contrast value for each pixel. Thedisparity or depth contrast value is the disparity or depth valuegradient at the respective pixel determined by the local variation indepth values associated with the adjacent pixels. The first 304 andsecond 306 processors are connected to a third processor 312 which isprogrammed to process the image in accordance with a non-lineardiffusion process based on the intensity contrast and depth contrastvalues determined by the respective first and second processors. Afourth processor 314 is connected to the third processor 312 forprocessing the image data simplified by the processor 312 to delineateand extract groups of neighbouring pixels representing physicallymeaning entities or objects contained within the image being processed.

[0057] The image processor of FIG. 3 is programmed to segment an imageby first simplifying the image and then extract objects from the imageby histogram based threshold analysis and extraction. An example of animage segmentation method will now be described with reference to theflowchart of FIG. 4.

[0058] Data defining a pair of stereoscopic images of a scene or asequence of images pairs constituting a video sequence are read frommemory 104 by the interface 302 of the image processor 102 in step 400.The image data is stored in the memory 104 as a set of grey levelvalues, one for each pixel. In step 402 the grey level values areprocessed by the processor 304 to determine the local variation inintensity in the region of each respective pixel to determine arespective contrast value for each of the pixels. Subsequently orsimultaneously, image data defining an image pair is processed by theprocessor 306, first in step 404 by processor 308 to determinerespective disparity vectors 210, and second in step 406 to determinerespective depth contrast values based on the local variation indisparity vector values in the region of each respective pixel. Step 404can be based on the method disclosed in the paper “Depth BasedSegmentation” IEEE Transaction on Circuits and Systems for Videoechnology, 7(1), February 1997, pp237-239, the contents of which areincorporated erein by reference.

[0059] The image. is simplified in step 408 by processor 312 accordingto a data ependent non-linear diffusion process. Step 408 involvesaltering the respective pixel intensity values by modifying therespective intensity contrast values according the corresponding depthcontrast values determined in steps 402 and 406 respectively. Theintensity values are altered towards an average intensity valuedetermined by the intensity values of the respective surrounding pixelsif the modified respective contrast value for the pixel is below acertain value. In this regard, the intensity contrast values aremodified such that pixels having a higher than average depth contrastvalue have their respective intensity values altered less than elementshaving a lower than average depth contrast value. Since step 408 isanalogous to a physical diffusion process the step is iterative andrepeats until a pre-determined equilibrium is achieved. The process ofstep 408 ultimately provides an image where the intensity values tend toan equilibrium value within the region corresponding to an object withinthe image, that is to say the or each object is represented by aseparate homogeneous region of intensity. The diffusion process isconsiderably reduced in regions corresponding to object boundaries sothat there is significant contrast in intensity between objects andobjects and between objects and background within an image of a scene.An example of the process of step 408 is described in greater detail inthe example described below.

[0060] In step 410 the processed image data of the simplified image isprocessed by the processor 314 to determine an image segmentation greylevel threshold value for image segmentation. In step 412 one or moreobjects are extracted from the image according to the modified intensityvalues of the respective pixels. Steps 410 and 412 may be implemented inaccordance with the histogram based segmentation method described in thepaper “An Amplitude Segmentation Method Based on the DistributionFunction of an Image”, Compute, Vision, Graphics and Image Processing,29, 47-59, 1985 mentioned above.

[0061] In the method described with reference to FIG. 4, an imagecontaining one or more structurally meaningful entities or objects isfirst simplified, that is to say the image is processed to removeinconsequential detail from the image, and then segmented into regionscontaining respective entities or objects. In the example described,image simplification is based on a modified non-linear diffusion processinvolving grey level intensity values of respective picture elements orpixels comprising the image. The process of step 408 will now bedescribed with reference to the following mathematical example.

EXAMPLE

[0062] Mathematically the process of diffusion can be described by thefollowing partial differential equation, known as the diffusionequation:

I _(t)=div(τ·∇I)   (1)

[0063] Equation (1) embodies two important properties: first, theequilibration property stated by Fick's law, φ=−τ·∇I , where ∇I is theconcentration gradient, φ is the flux, and τ is the diffusion tensor;and second, the continuity property given by I_(t)=−div(φ). Thus theconcentration I_(t) is equal to the −ve flux divergent.

[0064] In the context of the present invention the concentration I_(t)or I(x,y,t) is identified as the intensity (grey level value) at anyspatial sampling position I(x,y) of the evolved image at a time t.

[0065] If the diffusion tensor τ is constant over the whole image, thenEquation (1) describes a linear diffusion model,

I _(t) =c∇ ² I   (2)

[0066] where c is the diffusion constant and ∇²I the Laplacian of theimage intensity.

[0067] If the diffusion tensor τ in Equation (1) is defined as afunction of the local energy variation, that is the local imageintensity (or grey level value) gradient, at an image position (x, y),τ=ƒ(x, y,t), a diffusity function, then Equation (1) leads to,

I _(t)=∇·[ƒ(x,y,t)∇I]=div(ƒ(x,y,t)∇I)   (3)

[0068] Equation (3) defines a non-linear diffusion process in whichlocal averaging of grey level values is inhibited in regions of objectboundaries and diffusion velocity is controlled by the local intensity(or grey level value) gradient. Local averaging is the process ofaveraging the grey level values of adjacent pixels and replacing thecurrent grey level value of a pixel with this average value.

[0069] If the diffusity function f(.) is chosen as a continuouslydecreasing function of the image gradient, the diffusion processapproximates to a constant solution, or equilibrium, representing asimplified image with sharp boundaries. The amount of diffusion in eachpixel or image point is modulated by a function of the image gradient atthat point. Accordingly, image regions of high intensity contrastundergo less diffusion, whereas uniform regions are diffusedconsiderably.

[0070] Equation (3) may be combined together with a rapidly decreasingdiffusivity function: $\begin{matrix}{{{f\left( {{\nabla\quad I}}^{2} \right)} = \frac{1}{1 + {{{\nabla\quad I}}^{2}/K^{2}}}},} & (4)\end{matrix}$

[0071] and this diffusivity function leads to a flux function φ of theform: $\begin{matrix}{{{\varphi \left( {{\nabla\quad I}}^{2} \right)} = {{{f\left( {{\nabla\quad I}}^{2} \right)} \cdot {{\nabla\quad I}}} = \frac{{\nabla\quad I}}{1 + {{{\nabla\quad I}}^{2}/K^{2}}}}},} & (5)\end{matrix}$

[0072] Where K is a threshold value.

[0073] Thus the derivative of equation (5) is positive for ∥∇I∥<K andnegative for ∥∇I∥>K. Consequently the diffusion process behaves in aforward parabolic manner for ∥∇I∥<K, while it behaves in a backwardparabolic manner for ∥∇I∥>K. That is, Equation (5) presents acontrasting behaviour according to the magnitude of the image intensitygradient. It will sharpen edges with a local gradient greater than K,while smoothing edges with gradient magnitude lower than K. The value ofK can be determined experimentally. FIGS. 5a and 5 b show respective preand post processed images where the image has been processed using theabove-defined non-linear diffusion mathematical model.

[0074] The above model is improved by using the disparity valuesassociated with the respective pixels since these values varyconsiderably at object borders. In addition, the accuracy of disparityor depth estimation can be substantially increased at the object bordersgiven the known object outline from the intensity contrast values.

[0075] In one example of the present invention the disparity values areused to control the diffusion when non-linear diffusion is applied. FIG.5c shows the distribution of disparity values for the a stereoscopicimage pair corresponding to the image of FIG. 5 a. In thisrepresentation only the horizontal component of the respective disparityvectors is shown. The magnitude of the vector is represented by greyvalues. As shown, the approximate position of the object boundariescoincide with the image regions where the disparity variation is high.Thus, by analysing the local variation of the disparity vectors it ispossible to detect the position of the respective object borders.

[0076] The degree of smoothness ζ(z) of the disparity vectors at anysampling position z=(x, y), 500 in FIG. 5c, is obtained by measuring thestatistical variance of the disparity vectors inside a small observationwindow 502 centered at position 500. The size of the window 502 is forexample 8×8 pixels. The smoothness can be expressed as: $\begin{matrix}{{{(z)} = \sqrt{\sigma_{x}^{2} + \sigma_{y}^{2}}},} & (6)\end{matrix}$

[0077] where σ_(x) ² and σ_(y) ² are, respectively, the variances of thehorizontal and vertical components of the disparity vectors inside thewindow 502.

[0078] The diffusivity f(.) in Equation (4) is now defined as functionof a ζ-weighted image gradient ∥∇I∥_(ζ). That is, at each samplingposition z the magnitude of the image gradient is weighted by its localdisparity variance ζ(z). So if ζ_(max) is the maximum variance of theconsidered disparity field and g:[0, ζ_(max)]→[0,1], that is anyincreasing control function satisfying the two conditions g(0)=0 andg(ζ_(max))=1, then:

∥∇I∥_(ζ) ² =g(ζ(z))∥∇I∥ ².   (7)

[0079] There are several choices for the control function g. For examplea suitable family of functions is given by: $\begin{matrix}{{g(v)} = \left\{ {\begin{matrix}\left( {v/\sigma_{\max}^{2}} \right)^{n} & {{{if}\quad v} \leq C} \\1 & {otherwise}\end{matrix},} \right.} & (8)\end{matrix}$

[0080] where Cε(0, 1) is a threshold modulating the influence of ζ inthe diffusion process.

[0081] Applying the parabolic diffusion Equation (3) with diffusivityfunction ƒ(∥∇I(x,y,t)∥_(ζ) ²) an iterative disparity-driven ordepth-driven diffusion process model is defined.

[0082]FIG. 5d shows a simplified image of FIG. 5a whendisparity-controlled diffusion is applied according to the abovemathematical model.

[0083] It can be seen from FIG. 5d that the above-describeddisparity-driven non-linear diffusion model is particularly appropriatefor both object segmentation and pattern recognition image processing.Masks of complete physical objects can easily be extracted from theprocessed images using known histogram-based thresholding methods. Anexample of an extracted mask of the image of FIG. 5a is shown in FIG.5e.

[0084] Although the present invention has been described with referenceto stereoscopic disparity-driven non-linear diffusion it will beunderstood that other embodiments of the present invention could bereadily implemented by the person skilled in the art without furtherinventive contribution. For example, the depth values could instead beobtained by using an active imaging device comprising a low power laserrange finder to simultaneously obtain depth information relevant torespective pixels in an image or image sequence. In addition thedata-driven aspects of the above described non-linear diffusion processcould be readily implemented for video sequences using motion valuesinstead of the disparity values and determined in a similar way as thedisparity values but using monoscopic sequential frames of a videosequence instead of stereoscopic image pairs, for example.

1. An method of processing an image recorded by an imaging device; saidmethod comprising the steps of: a) providing an image comprising anarray of adjacent elements each corresponding to a respective part ofthe image and having a respective intensity value associated therewith;b) processing said intensity values to determine an intensity contrastvalue for each respective element according to differences in intensityvalues between respective adjacent elements; c) providing a depth valuefor each element corresponding to the distance between the imagingdevice and at least part of an image forming object represented in theimage by the respective element; d) processing said depth values todetermine a depth contrast value for each respective element accordingto differences in depth values between respective adjacent elements;and, e) processing said intensity contrast values and said depthcontrast values to identify at least one area of the image correspondingto one or more respective objects in the image being processed.
 2. Amethod according to claim 1 wherein said depth values are determinedaccording to the spacing between corresponding points on a stereoscopicimage pair.
 3. A method according to claim 2 wherein the spacing betweensaid points is determined by matching said corresponding points andestimating a vector value for the relative spacing and direction of saidpoints.
 4. A method according to any preceding claim wherein said areais determined by identifying an outline of said respective object orobjects in the image.
 5. A method according to any preceding claimwherein step e) comprises the step of altering the intensity values ofeach respective element in accordance with the respective intensitycontrast value and the respective depth contrast value of the element.6. A method according to claim 5 wherein the step of altering theintensity values comprises the step of modifying the respectiveintensity contrast values according to the respective depth contrastvalues, and altering the intensity values of the respective elementstowards an average intensity value determined by intensity values ofsurrounding elements if the respective modified intensity contrast valueof the element is below a threshold value.
 7. A method according toclaim 6 wherein the intensity contrast values are modified such thatelements having a higher than average depth contrast value have theirrespective intensity values altered less than elements having a lowerthan average depth contrast value.
 8. A method according to anypreceding claim wherein step e) comprises a non-linear diffusion processfor altering element intensity values in accordance with respectiveintensity contrast values modified in accordance with respective depthcontrast values.
 9. A method according to any one of claims 5 to 8further comprising the step of de-lineating said object or objects fromthe image.
 10. A method according to claim 9 wherein the delineatingstep comprises the steps of: determining a distribution of the alteredintensity values of the respective elements; determining a thresholdvalue or range of values to include all the intensity values of therespective elements of at least one identified area in the image; and,selecting elements having modified intensity values within the thresholdrange or above or below said threshold value; and delineating image datarelating to said selected elements from image data relating to theremaining elements.
 11. An method of processing an image in accordancewith a non-linear diffusion process; said method comprising the stepsof: a) providing an image comprising an array of adjacent elements eachorresponding to a respective part of the image and having a respectiveintensity value ssociated therewith; b) processing said intensity valuesto determine an intensity contrast value for each respective elementaccording to differences in intensity values between respective adjacentelements; c) identifying from said array of elements object definingelements corresponding to points on respective objects in the image; d)altering said element intensity values in accordance said respectiveintensity contrast values, whereby said elements are altered to a lesseror greater extent in dependence on whether said respective element is anobject element.
 12. A method according to claim 11 wherein step c)comprises the step of determining a depth value associated with adisparity field for each element in the image and identifying saidobject elements from said depth values.
 13. A method according to claim11 wherein step c) comprises the step of determining a motion valueassociated with a motion vector in a video sequence for each element inthe image and identifying said object elements from said motion values.14. A system configured to implement a method according to any precedingclaim
 15. An image processing system for processing an image recorded byan imaging device; said system comprising: a) a data receiver forreceiving data relating to an image comprising an array of adjacentelements each corresponding to a respective part of the image and havinga respective intensity value associated therewith; an intensity valueprocessor configured to determine an intensity contrast value for eachrespective element according to differences in intensity values betweenrespective adjacent elements; a depth value processor configured todetermine a depth value for each element corresponding to the distancebetween the imaging device and at least part of an image forming objectrepresented in the image by the respective element; a depth contrastvalue processor configured to determine a depth contrast value for eachrespective element according to differences in depth values betweenrespective adjacent elements; and, an object segment processorconfigured to process intensity contrast values and said depth contrastvalues to identify at least one area of the image corresponding to oneor more respective objects in the image being processed.